Nicolas Vandapel

Natural terrain classification using three‐dimensional ladar data for ground robot mobility

In recent years, much progress has been made in outdoor autonomous navigation. However, safe navigation is still a daunting challenge in terrain containing vegetation. In this paper, we f ocus on the segmentation of ladar data into three classes using local three-dimensional point cloud statistics. The classes are: ”scatter” to represent porous volumes such as grass and tree canopy, ”linear” to capture thin objects like wires or tree branches, and finally ”surface” to capture solid objects like ground surface, rocks or large trunks. We present the details of the proposed method, and the modifications we made to implement it on-board an autonom ous ground vehicle for real-time data processing. Finally, we present results produced from different sta tionary laser sensors and from field tests using an unmanned ground vehicle.

Natural terrain classification using 3-d ladar data

Because of the difficulty of interpreting laser data in a meaningful way, safe navigation in vegetated terrain is still a daunting challenge. In this paper, we focus on the segmentation of ladar data using local 3-D point statistics into three classes: clutter to capture grass and tree canopy, linear to capture thin objects like wires or tree branches, and finally surface to capture solid objects like ground terrain surface, rocks or tree trunks. We present the details of the method proposed, the modifications we made to implement it on-board an autonomous ground vehicle. Finally, we present results from field tests using this rover and results produced from different stationary laser sensors.

Contextual classification with functional Max-Margin Markov Networks

We address the problem of label assignment in computer vision: given a novel 3D or 2D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information to yield improved classification results over locally independent classifiers. In this work we adapt a functional gradient approach for learning high-dimensional parameters of random fields in order to perform discrete, multi-label classification. With this approach we can learn robust models involving high-order interactions better than the previously used learning method. We validate the approach in the context of point cloud classification and improve the state of the art. In addition, we successfully demonstrate the generality of the approach on the challenging vision problem of recovering 3-D geometric surfaces from images.

Improving robot navigation through self‐supervised online learning

In mobile robotics, there are often features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations. Overhead imagery data, for instance, have the potential to greatly enhance autonomous robot navigation in complex outdoor environments. In practice, reliable and effective automated interpretation of imagery from diverse terrain, environmental conditions, and sensor varieties proves challenging. Similarly, fixed techniques that successfully interpret on-board sensor data across many environments begin to fail past short ranges as the density and accuracy necessary for such computation quickly degrade and features that are able to be computed from distant data are very domain specific. We introduce an online, probabilistic model to effectively learn to use these scope-limited features by leveraging other features that, while perhaps otherwise more limited, generalize reliably. We apply our approach to provide an efficient, self-supervised learning method that accurately predicts traversal costs over large areas from overhead data. We present results from field testing on-board a robot operating over large distances in various off-road environments. Additionally, we show how our algorithm can be used offline with overhead data to produce a priori traversal cost maps and detect misalignments between overhead data and estimated vehicle positions. This approach can significantly improve the versatility of many unmanned ground vehicles by allowing them to traverse highly varied terrains with increased performance.

Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields

Contextual reasoning through graphical models such as Markov Random Fields often show superior performance against local classifiers in many domains. Unfortunately, this performance increase is often at the cost of time consuming, memory intensive learning and slow inference at testing time. Structured prediction for 3-D point cloud classification is one example of such an application. In this paper we present two contributions. First we show how efficient learning of a random field with higher-order cliques can be achieved using subgradient optimization. Second, we present a context approximation using random fields with high-order cliques designed to make this model usable online, onboard a mobile vehicle for environment modeling. We obtained results with the mobile vehicle on a variety of terrains, at 1/3 Hz for a map 25 × 50 meters and a vehicle speed of 1–2 m/s.

Classifier fusion for outdoor obstacle detection

This work describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation. We are focusing on outdoor obstacle detection, and we present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems. We are combining color and infrared (IR) imagery with range information from a laser range finder. We show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and non-obstacle for example). We train classifiers on different subsets of the features we extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input. We present experimental results we obtained on data collected with both the experimental unmanned vehicle (XUV) and a CMU developed robotic tractor.

Moving object detection with laser scanners

The detection and tracking of moving objects is an essential task in robotics. The CMU-RI Navlab group has developed such a system that uses a laser scanner as its primary sensor. We will describe our algorithm and its use in several applications. Our system worked successfully on indoor and outdoor platforms and with several different kinds and configurations of two-dimensional and three-dimensional laser scanners. The applications vary from collision warning systems, people classification, observing human tracks, and input to a dynamic planner. Several of these systems were evaluated in live field tests and shown to be robust and reliable.

Scale selection for classification of point-sampled 3D surfaces

Three-dimensional ladar data are commonly used to perform scene understanding for outdoor mobile robots, specifically in natural terrain. One effective method is to classify points using features based on local point cloud distribution into surfaces, linear structures or clutter volumes. But the local features are computed using 3D points within a support-volume. Local and global point density variations and the presence of multiple manifolds make the problem of selecting the size of this support volume, or scale, challenging. In this paper, we adopt an approach inspired by recent developments in computational geometry (Mitra et al., 2005) and investigate the problem of automatic data-driven scale selection to improve point cloud classification. The approach is validated with results using data from different sensors in various environments classified into different terrain types (vegetation, solid surface and linear structure).

Analysis and Removal of Artifacts in 3-D LADAR Data

Errors in laser based range measurements can be divided into two categories: intrinsic sensor errors (range drift with temperature, systematic and random errors), and errors due to the interaction of the laser beam with the environment. The former have traditionally received attention and can be modeled. The latter in contrast have long been observed but not well characterized. We propose to do so in this paper. In addition, we present a sensor independent method to remove such artifacts. The objective is to improve the overall quality of 3-D scene reconstruction to perform terrain classification of scenes with vegetation.

Unmanned Ground Vehicle Navigation Using Aerial Ladar Data

In this paper, we investigate the use of overhead high-resolution three-dimensional (3D) data for enhancing the performances of an unmanned ground vehicle (UGV) in vegetated terrains. Data were collected using an airborne laser and provided prior to the robot mission. Through extensive and exhaustive field testing, we demonstrate the significance of such data in two areas: robot localization and global path planning. Absolute localization is achieved by registering 3D local ground ladar data with the global 3D aerial data. The same data are used to compute traversability maps that are used by the path planner. Vegetation is filtered both in the ground data and in the aerial data in order to recover the load bearing surface.